Non-convex penalty functions can promote sparsity more strongly than convex penalty functions [1]}, [2]}.
This subsection briefly describes the non-convex penalty functions which will be used in the proposed approach.
The smoothed non-convex penalty function \(\phi _{\epsilon }: \mathbb {R}\rightarrow \mathbb {R}_{+}\)  is used in this work.
Table REF  gives several examples of the functions defined by
\(\phi _{\epsilon }(u ; a) : = \phi ( \sqrt{ u^2 +\epsilon } ; a ), \quad \epsilon > 0,\) 
